Herbal medicines are widely used in our community. A survey of Australian consumers indicated that 60% had used complementary and/or alternative medicines in the past year with the majority not informing their doctor that they were using herbal medicines. Little is known about the potentially serious consequences of interactions between herbal and conventional medicines. Warfarin has an important role in treating people with heart disease, yet it has a narrow therapeutic range, is highly bound to plasma proteins, and is metabolised by cytochrome P450. This creates the potential for life-threatening interactions with other drugs and foods leading to excessive bleeding. Hence, warfarin is one of the most frequently investigated drugs for interaction studies. Early clinical reports suggest that there exists the potential for an interaction between warfarin and four herbal medicines: St John�s wort, ginseng, ginkgo and ginger. However, these herb-drug combinations have never been conclusively studied. The two clinical studies conducted as part of this research had an identical study design. Twenty-four healthy male subjects were recruited into the two separate studies. This was an open label, three-way crossover randomised study in twelve healthy male subjects, who received a single 25 mg dose of warfarin alone or after 14 days pre-treatment with St John�s wort, or 7 days pre-treatment with ginseng. Dosing with St John�s wort or ginseng was continued for 7 days after administration of the warfarin dose in study I or who received a single 25 mg dose of warfarin alone or after 7 days pre-treatment with recommended doses of ginkgo or ginger from single ingredient products of known quality. Dosing with ginkgo or ginger was continued for 7 days after administration of the warfarin dose in study II. Platelet aggregation, international normalised ratio (INR) of prothrombin time, warfarin enantiomer protein binding, warfarin enantiomer concentrations in plasma and S-7-hydroxywarfarin concentration in urine were measured in both studies. Statistical comparisons were made using ANOVA and 95% confidence interval (CI) for mean value and 90% CI for geometric mean ratio value are reported. n study I, the mean (95% CI) apparent clearance of S-warfarin after warfarin alone or with St John�s wort or ginseng were, respectively, 198 (174 � 223) ml/h, 269 (241 � 297) ml/h and 220 (201 � 238) ml/h. The respective apparent clearances of R-warfarin were 110 (94 � 126) ml/h, 142 (123 � 161) ml/h and 119 (106 � 131) ml/h. The mean ratio of apparent clearance for S-warfarin was 1.29 (1.16-1.46) and for R-warfarin was 1.23 (1.11-1.37) when St John�s wort was co-administered. The mean ratio of AUC0-168 of INR was 0.79 (0.70 - 0.95) when St John�s wort was co-administered. The urinary excretion ratio of S-7-hydroxywarfarin after administration of warfarin alone was 0.04 (0.03 � 0.06) mg/h and there was no significant difference following treatment with either St John�s wort 0.03 (0.02 � 0.04) mg/h or ginseng 0.03 (0.02 � 0.04) mg/h. The ratio of geometric means for S-7-hydroxywarfarin UER was 0.82 (0.61-1.12) for St John�s wort, and 0.68 (0.50-0.91) for ginseng. St John�s wort and ginseng did not affect the apparent volumes of distribution or protein binding of warfarin enantiomers. In study II, the mean (95% CI) apparent clearance of S-warfarin after warfarin alone, with ginkgo or ginger were 189 (167 � 210) ml/h, 200 (173 � 227) ml/h and 201 (171 � 231) ml/h, respectively. The respective apparent clearances of R-warfarin were 127 (106 � 149) ml/h, 126 (111 � 141) ml/h and 131 (106 � 156) ml/h. The mean ratio of apparent clearance for S-warfarin was 1.05 (0.98 -1.12) and for R-warfarin was 1.00 (0.93 -1.08) when co-administered with ginkgo. The mean ratio of AUC0-168 of INR was 0.93 (0.81 -1.05) when co-administered with ginkgo. The mean ratio of apparent clearance for S-warfarin was 1.05 (0.97 -1.13) and for R-warfarin was 1.02 (0.95 -1.10) when co-administered with ginger. The mean ratio of AUC0-168 of INR was 1.01 (0.93 -1.15) when co-administered with ginger. The urinary excretion ratio (UER) of S-7-hydroxywarfarin after administration of warfarin alone was 0.04 (0.03 � 0.05) mg/h and there was no significant difference following treatment with either ginkgo 0.04 (0.03 � 0.04) mg/h or ginger 0.03 (0.02 � 0.04) mg/h. The ratio of geometric means for S-7-hydroxywarfarin UER was 1.07 (0.69-1.67) for ginkgo, and 1.00 (0.64-1.56) for ginger. Ginkgo and ginger did not affect the apparent volumes of distribution or protein binding of either S-warfarin or R-warfarin. In conclusion, St John�s wort significantly induced the apparent clearance of both S-warfarin and R-warfarin, which in turn resulted in a significant reduction in the pharmacological effect of rac-warfarin. Ginseng, ginkgo and ginger at recommended doses affect neither clotting status, nor the pharmacokinetics or pharmacodynamics of either S-warfarin or R-warfarin in healthy subjects.

Atomic force microscopy (AFM) is a particularly useful tool, for obtaining structural information about drug-nucleic acid interactions. The mode of drug binding intercalation versus groove binding can be determined from images acquired on individual DNA molecules as the length of a DNA molecule increases in direct proportion to the number of intercalators bound to it. The efforts of this research were directed toward elucidating the mode of binding of a series of drugs based on polymers of naphthalenetetracarboxyl diimide (NDI) interacting with a linearized DNA plasmid. During the course of the investigation of these drugs, DNA intercalation was confirmed as the mode of binding and the binding affinity estimated. Unexpectedly, concentration-dependent formation of secondary DNA structures including condensates was observed. DNA toroids, spheres, and rods were imaged and measured. Conformations that are believed to be intermediate condensate forms were also identified at lower poly-NDI concentrations. Models for the DNA condensation process have been proposed. Ultimately, this research furthers the understanding of DNA condensation which can be applied to gene delivery systems and anti-viral agents. It may also help direct the development of better drugs based on the insight of poly-intercalators interactions with DNA.

An estimated 70,000 preventable medication errors occur in Canada annually, causing up to 23,750 deaths. Medication errors increase when the number of medications being administered increases. Therefore, people with multi-morbidity who take several medications at once are more vulnerable to medication errors. Medication errors can be prevented by developing and managing an efficient healthcare system integrated with technology. Near Field Communication (NFC) technology, in particular, has been shown to improve the quality of health care and increase patient safety. NFC has a powerful ability to identify and track objects such as patients and medications; its identification and tracking abilities give it significant potential especially in detecting drug interaction and drug allergy. The main objective of this thesis is to present a novel solution using NFC-enabled smartphone integrated with NFC application to detect and update drug allergies and drug interactions for people with multi-morbidity during medication administration. / The system has been implemented using Samsung Nexus S smartphone with Android 2.3.6 platform, MIFARE Classic 1K tags, and a database populated with 10 patients’ record and 30 medications. The system was validated for the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and computational and communicational cost.

This article presents a study conducted on data containing drug concentrations. The data was obtained from femoral venous blood samples collected at medico legal autopsies in Sweden. Cases positive for antidepressant drugs were scrutinized and divided in to two groups for 15 antidepressant drugs: B‑cases, where the cause of death was intoxication with more than one drug detected in the blood sample. C‑cases, where the cause of death was NOT intoxication and at least one drug (the antidepressant) was detected in the blood sample. This data was then processed to find frequencies of concomitant drugs taken together with the antidepressant drugs. Frequencies of the most common concomitant drugs were then compared between B-cases and C-cases for each antidepressant drug. This revealed that the drugs dextropropoxyphene, ethanol, codeine, flunitrazepam, paracetamol, propiomazine and alimemazine were signifcantly more common as concomitant drugs in B-cases (intoxications) than in C‑cases (non‑intoxications). With regards to unknown interactions the most interesting combinations were: Propiomazine with mirtazapine, venlafaxine, citalopram or fluoxetine; Paracetamol with paroxetine; Flunitrazepam with mirtazapine, venlafaxine or citalopram; Codeine with mirtazapine or sertraline. These combinations should be further investigated.

Herbal medicines are widely used in our community. A survey of Australian consumers indicated that 60% had used complementary and/or alternative medicines in the past year with the majority not informing their doctor that they were using herbal medicines. Little is known about the potentially serious consequences of interactions between herbal and conventional medicines. Warfarin has an important role in treating people with heart disease, yet it has a narrow therapeutic range, is highly bound to plasma proteins, and is metabolised by cytochrome P450. This creates the potential for life-threatening interactions with other drugs and foods leading to excessive bleeding. Hence, warfarin is one of the most frequently investigated drugs for interaction studies. Early clinical reports suggest that there exists the potential for an interaction between warfarin and four herbal medicines: St John�s wort, ginseng, ginkgo and ginger. However, these herb-drug combinations have never been conclusively studied. The two clinical studies conducted as part of this research had an identical study design. Twenty-four healthy male subjects were recruited into the two separate studies. This was an open label, three-way crossover randomised study in twelve healthy male subjects, who received a single 25 mg dose of warfarin alone or after 14 days pre-treatment with St John�s wort, or 7 days pre-treatment with ginseng. Dosing with St John�s wort or ginseng was continued for 7 days after administration of the warfarin dose in study I or who received a single 25 mg dose of warfarin alone or after 7 days pre-treatment with recommended doses of ginkgo or ginger from single ingredient products of known quality. Dosing with ginkgo or ginger was continued for 7 days after administration of the warfarin dose in study II. Platelet aggregation, international normalised ratio (INR) of prothrombin time, warfarin enantiomer protein binding, warfarin enantiomer concentrations in plasma and S-7-hydroxywarfarin concentration in urine were measured in both studies. Statistical comparisons were made using ANOVA and 95% confidence interval (CI) for mean value and 90% CI for geometric mean ratio value are reported. n study I, the mean (95% CI) apparent clearance of S-warfarin after warfarin alone or with St John�s wort or ginseng were, respectively, 198 (174 � 223) ml/h, 269 (241 � 297) ml/h and 220 (201 � 238) ml/h. The respective apparent clearances of R-warfarin were 110 (94 � 126) ml/h, 142 (123 � 161) ml/h and 119 (106 � 131) ml/h. The mean ratio of apparent clearance for S-warfarin was 1.29 (1.16-1.46) and for R-warfarin was 1.23 (1.11-1.37) when St John�s wort was co-administered. The mean ratio of AUC0-168 of INR was 0.79 (0.70 - 0.95) when St John�s wort was co-administered. The urinary excretion ratio of S-7-hydroxywarfarin after administration of warfarin alone was 0.04 (0.03 � 0.06) mg/h and there was no significant difference following treatment with either St John�s wort 0.03 (0.02 � 0.04) mg/h or ginseng 0.03 (0.02 � 0.04) mg/h. The ratio of geometric means for S-7-hydroxywarfarin UER was 0.82 (0.61-1.12) for St John�s wort, and 0.68 (0.50-0.91) for ginseng. St John�s wort and ginseng did not affect the apparent volumes of distribution or protein binding of warfarin enantiomers. In study II, the mean (95% CI) apparent clearance of S-warfarin after warfarin alone, with ginkgo or ginger were 189 (167 � 210) ml/h, 200 (173 � 227) ml/h and 201 (171 � 231) ml/h, respectively. The respective apparent clearances of R-warfarin were 127 (106 � 149) ml/h, 126 (111 � 141) ml/h and 131 (106 � 156) ml/h. The mean ratio of apparent clearance for S-warfarin was 1.05 (0.98 -1.12) and for R-warfarin was 1.00 (0.93 -1.08) when co-administered with ginkgo. The mean ratio of AUC0-168 of INR was 0.93 (0.81 -1.05) when co-administered with ginkgo. The mean ratio of apparent clearance for S-warfarin was 1.05 (0.97 -1.13) and for R-warfarin was 1.02 (0.95 -1.10) when co-administered with ginger. The mean ratio of AUC0-168 of INR was 1.01 (0.93 -1.15) when co-administered with ginger. The urinary excretion ratio (UER) of S-7-hydroxywarfarin after administration of warfarin alone was 0.04 (0.03 � 0.05) mg/h and there was no significant difference following treatment with either ginkgo 0.04 (0.03 � 0.04) mg/h or ginger 0.03 (0.02 � 0.04) mg/h. The ratio of geometric means for S-7-hydroxywarfarin UER was 1.07 (0.69-1.67) for ginkgo, and 1.00 (0.64-1.56) for ginger. Ginkgo and ginger did not affect the apparent volumes of distribution or protein binding of either S-warfarin or R-warfarin. In conclusion, St John�s wort significantly induced the apparent clearance of both S-warfarin and R-warfarin, which in turn resulted in a significant reduction in the pharmacological effect of rac-warfarin. Ginseng, ginkgo and ginger at recommended doses affect neither clotting status, nor the pharmacokinetics or pharmacodynamics of either S-warfarin or R-warfarin in healthy subjects.

Diss. (sammanfattning) Uppsala : Uppsala universitet, 2006. / Härtill 4 uppsatser.

A case is presented of a fatal drug interaction caused by ingestion of oxycodone (Oxycontin®) and clonazepam (Klonapin®). Oxycodone is an opium alkaloid used in long-term pain management therapy. Clonazepam is a benzodiazepine used for the treatment of seizures and panic disorders. The Drug Abuse Warning Network (DAWN) has reported an increase of 108% in the last two years of emergency department episodes related to Oxycontin®. Six billion prescriptions were written for Oxycontin® in the year 2000, an 18-fold increase from four years previous (1). Oxycontin has recently gained enormous notoriety at the local and national levels; however, there are very few previously documented cases of lethal drug interactions between oxycodone and clonazepam. Synergistic effects between these two drugs are postulated to arise from different agonistic mechanisms producing similar physiological changes. It is also theorized that clonazepam may inhibit the metabolism of oxycodone. A 38-year-old white female was found dead in Jefferson County, Tennessee in March of 2001. The deceased had physical evidence of previous drug abuse and positive serological findings of hepatitis B and C. Prescription pill bottles filled under the name of the deceased, as well as another name, were found with the body. Serum, urine and gastric contents from the deceased were screened for numerous drugs and metabolites using a combination of thin layer chromatography and immunoassay techniques (EMIT and FPIA). Analysis of biological specimens from the deceased revealed the presence of: benzodiazepines, opiates (oxycodone), and trazodone metabolites in the serum; cannabinoids, benzodiazepines, opiates (oxycodone), trazodone, trazodone metabolites, nicotine, and nicotine metabolite in the urine; and benzodiazepines, opiates (oxycodone), nicotine, and nicotine metabolite in the gastric contents. Quantitative analyses for clonazepam was performed by high performance liquid chromatography (HPLC) and revealed a plasma concentration of 1.41 μg/mL. Plasma oxycodone and urine 11-nor-carboxy-delta-9-tetrahydrocannabinol concentrations were determined by gas chromatography/mass spectrometry and revealed concentrations of 0.60 μg/mL and 27.9 ng/mL, respectively. The deceased had pathologies consistent with severe central nervous system (CNS) and respiratory depression produced by high concentrations of clonazepam and oxycodone including collapsed lungs, aspirated mucus, and heart failure. The pathologies were sufficient to cause death, which was officially attributed to a drug overdose; however, the manner of death was unknown.

Indiana University-Purdue University Indianapolis (IUPUI) / Drug-Drug Interaction (DDI) is one of the major causes of adverse drug reaction (ADR) and has been demonstrated to threat public health. It causes an estimated 195,000 hospitalizations and 74,000 emergency room visits each year in the USA alone. Current DDI research aims to investigate different scopes of drug interactions: molecular level of pharmacogenetics interaction (PG), pharmacokinetics interaction (PK), and clinical pharmacodynamics consequences (PD). All three types of experiments are important, but they are playing different roles for DDI research. As diverse disciplines and varied studies are involved, interaction evidence is often not available cross all three types of evidence, which create knowledge gaps and these gaps hinder both DDI and pharmacogenetics research. In this dissertation, we proposed to distinguish the three types of DDI evidence (in vitro PK, in vivo PK, and clinical PD studies) and identify all knowledge gaps in experimental evidence for them. This is a collective intelligence effort, whereby a text mining tool will be developed for the large-scale mining and analysis of drug-interaction information such that it can be applied to retrieve, categorize, and extract the information of DDI from published literature available on PubMed. To this end, three tasks will be done in this research work: First, the needed lexica, ontology, and corpora for distinguishing three different types of studies were prepared. Despite the lexica prepared in this work, a comprehensive dictionary for drug metabolites or reaction, which is critical to in vitro PK study, is still lacking in pubic databases. Thus, second, a name entity recognition tool will be proposed to identify drug metabolites and reaction in free text. Third, text mining tools for retrieving DDI articles and extracting DDI evidence are developed. In this work, the knowledge gaps cross all three types of DDI evidence can be identified and the gaps between knowledge of molecular mechanisms underlying DDI and their clinical consequences can be closed with the result of DDI prediction using the retrieved drug gene interaction information such that we can exemplify how the tools and methods can advance DDI pharmacogenetics research. / 2 years

A case is presented of a fatal drug interaction caused by ingestion of clozapine (Clozaril(TM)) and fluoxetine (Prozac(TM)). Clozapine is a tricyclic dibenzodiazepine derivative used as an 'atypical antipsychotic' in the treatment of severe paranoid schizophrenia. Fluoxetine is a selective serotonin reuptake inhibitor used for the treatment of major depression. Clinical studies have proven that concomitant administration of fluoxetine and clozapine produces increased plasma concentrations of clozapine and enhances clozapine's pharmacological effects due to suspected inhibition of clozapine metabolism by fluoxetine. Blood, gastric, and urine specimens were analyzed for fluoxetine by gas chromatography/mass spectrometry (GC/MS) and for clozapine by gas-liquid chromatography (GLC). Clozapine concentrations were: plasma, 4.9 μg/mL; gastric contents, 265 mg; and urine, 51.5 μg/mL. Fluoxetine concentrations were: blood, 0.7 μg/mL; gastric contents, 3.7 mg; and urine 1.6 μg/mL. Norfluoxetine concentrations were: blood, 0.6 μg/mL, and none detected in the gastric contents or urine. Analysis of the biological specimens for other drugs revealed the presence of ethanol (blood, 35 mg/dL; vitreous, 56 mg/dL; and urine 153 mg/dL) and caffeine (present in all specimens). The combination of these drugs produced lethal concentrations of clozapine and high therapeutic to toxic concentrations of fluoxetine. The deceased had pulmonary edema, visceral vascular congestion, paralytic ileus, gastroenteritis and eosinophilia. These conditions are associated with clozapine toxicity. The combined central nervous system, respiratory and cardiovascular depression of these drugs was sufficient to cause death. The death was determined to be a clozapine overdose due to a fatal drug interaction.

In recent years, several new oral anticancer drugs have been approved, many via an accelerated approval process. These new agents have the potential for drug interactions, but lack of familiarity with these drugs by clinicians may increase the risk for drug interactions. We describe an interaction between the new anticancer agent idelalisib (CYP 3A4 inhibitor) and diazepam (CYP 3A4 substrate) that resulted in altered mental status and type II respiratory failure resulting in hospitalization. After discontinuation of both agents, the patient recovered quickly. Idelalisib was reinitiated after discharge. Lorazepam was substituted for diazepam since it is not metabolized via CYP 3A4. Both agents were tolerated well thereafter. This interaction was only flagged by two of four commonly used drug interaction databases. Clinicians should exercise caution with initiating new oral anticancer agents and consider the potential for drug interactions without solely relying on drug interaction databases.